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Automatic Segmentation and Quantitative Analysis of the Articular Cartilages From Magnetic Resonance Images of the Knee

Automatic Segmentation and Quantitative Analysis of the Articular Cartilages From Magnetic Resonance Images of the Knee,10.1109/TMI.2009.2024743,IEEE

Automatic Segmentation and Quantitative Analysis of the Articular Cartilages From Magnetic Resonance Images of the Knee   (Citations: 5)
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In this paper, we present a segmentation scheme that automatically and accurately segments all the cartilages from magnetic resonance (MR) images of nonpathological knees. Our scheme involves the automatic segmentation of the bones using a three-dimensional active shape model, the extraction of the expected bone-cartilage interface (BCI), and cartilage segmentation from the BCI using a deformable model that utilizes localization, patient specific tissue estimation and a model of the thickness variation. The accuracy of this scheme was experimentally validated using leave one out experiments on a database of fat suppressed spoiled gradient recall MR images. The scheme was compared to three state of the art approaches, tissue classification, a modified semi-automatic watershed algorithm and nonrigid registration (B-spline based free form deformation). Our scheme obtained an average Dice similarity coefficient (DSC) of (0.83, 0.83, 0.85) for the (patellar, tibial, femoral) cartilages, while (0.82, 0.81, 0.86) was obtained with a tissue classifier and (0.73, 0.79, 0.76) was obtained with nonrigid registration. The average DSC obtained for all the cartilages using a semi-automatic watershed algorithm (0.90) was slightly higher than our approach (0.89), however unlike this approach we segment each cartilage as a separate object. The effectiveness of our approach for quantitative analysis was evaluated using volume and thickness measures with a median volume difference error of (5.92, 4.65, 5.69) and absolute Laplacian thickness difference of (0.13, 0.24, 0.12) mm.
Journal: IEEE Transactions on Medical Imaging - TMI , vol. 29, no. 1, pp. 55-64, 2010
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    • ...Within the literature, segmentation of bones in the knee certainly has been considered in the past [2, 3, 4]. To the best of our knowledge, howevernone of this work has explicitly been concernedwith the weak edge problem that is considered here...

    Jincheng Panget al. A curve evolution method for identifying weak edges with applications ...

    • ...Fripp et al. [21] used a three-dimensional statistical shape model to segment bone and a model of thickness variation to yield cartilage boundary...

    Kasper Marstalet al. Semi-automatic segmentation of knee osteoarthritic cartilage in magnet...

    • ...Following application of a 3-D median filter (radius one voxel) to remove local noise, image intensities are normalized as suggested in [19], the values between the image-intensity minimum and maximum are linearly stretched to cover the range of 0–1000...
    • ...Additional features were added, including the physical distances of a location from the bone surface [19] and three eigenvalues of the Hessian image...
    • ...Inspired by the work of Folkesson and Fripp [19], [23], a random forest classifier [21] was trained for each bone using voxels surrounding the corresponding bone boundaries [Fig. 5(b)]...
    • ...SSM [34]. In this work, when given a pre-segmented bone-cartilage interfaces, it took about an hour to segment the cartilage surface on an MR image with size . More recently, Fripp et al. [19] employed atlas-based bone registration and bone–cartilage interfaces (BCI) were segmented by ASMs...
    • ...The presented method obtains the segmentation relatively quickly and is much faster compared to the two previously reported methods of Dam and Fripp [19], [36]...
    • ...Fripp reported DSC values of 0.85, 0.83, and 0.83 for the femoral, tibial, and patellar cartilages from 20 healthy SPGR MR images [19]...

    Yin Yinet al. LOGISMOS - Layered Optimal Graph Image Segmentation of Multiple Object...

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